首页|Corteva Agriscience Reports Findings in Machine Learning (High-Throughput Image- Based Assay for Identifying In Vitro Hepatocyte Microtubule Disruption)
Corteva Agriscience Reports Findings in Machine Learning (High-Throughput Image- Based Assay for Identifying In Vitro Hepatocyte Microtubule Disruption)
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New research on Machine Learning is th e subject of a report. According to news reporting from Indianapolis, Indiana, b y NewsRx journalists, research stated, "Disruption of microtubule stability in m ammalian cells may lead to genotoxicity and carcinogenesis. The ability to scree n for microtubule destabilization or stabilization is therefore a useful and eff icient approach to aid in the design of molecules that are safe for human health ." The news correspondents obtained a quote from the research from Corteva Agriscie nce, "In this study, we developed a high-throughput 384-well assay combining imm unocytochemistry with high-content imaging to assess microtubule disruption in t he metabolically competent human liver cell line: HepaRG. To enhance analysis th roughput, we implemented a supervised machine learning approach using a curated training library of 180 compounds. A majority voting ensemble of eight machine l earning classifiers was employed for predicting microtubule disruptions. Our pre diction model achieved over 99.0% accuracy and a 98.4% F1 score, which reflects the balance between precision and recall for in-sample validation and 93.5 % accuracy and a 94.3% F1 score f or out-of-sample validation."
IndianapolisIndianaUnited StatesNo rth and Central AmericaCellular StructuresCyborgsCytoplasmCytoplasmic St ructuresCytoskeletonEmerging TechnologiesHepatocytesIntracellular SpaceMachine LearningMicrotubules
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